🤖 AI Summary
This study addresses the conceptual ambiguity and unclear interrelationships among stance, sentiment, framing, and argumentation—key perspective-related constructs in natural language processing—that stem from the absence of a unified theoretical framework. Through a systematic literature review and conceptual analysis, the work proposes a set of distinguishing attributes and, for the first time, formulates a linear hierarchical model that clarifies the intrinsic logical relationships among these constructs. The resulting model offers a coherent theoretical foundation and a conceptual navigation tool for perspective research, enabling scholars to select appropriate operationalization pathways aligned with their specific task objectives. This advancement enhances the systematicity and effectiveness of research on linguistic perspectives.
📝 Abstract
The same event can be reported from different perspectives depending on the experiences, background, and beliefs of the writer or speaker. A variety of NLP areas engage with perspectives, spanning from text analysis to algorithm optimization. A wide range of operative concepts (such as stances, sentiment, frames, and arguments) has been used to capture perspectives in texts, however the precise relationships among those concepts remain unclear. Arguably, a deeper theoretical understanding of these concepts would empower more effective research on perspectives. In this paper, we address this gap by reviewing the space of perspectives in NLP and defining a set of properties that help distinguishing perspective-related concepts. Our analysis leads us to posit a hierarchy which organizes these concepts linearly along a single axis. Finally, we show how this principled conceptual hierarchy can help researchers navigate the field and select operationalizations of perspective that align with their specific research objectives.